Big Data Research最新文献_第2页

Bipartite graph partitioning and spatial bootstrapping methods: A case study of innovative startups 二部图划分与空间自举方法：以创新创业公司为例

IF 3.5 3区计算机科学

Big Data Research Pub Date : 2025-05-16 DOI: 10.1016/j.bdr.2025.100533

Alessio Bumbea , Andrea Mazzitelli , Giuseppe Espa , Alessandro Rinaldi

{"title":"Bipartite graph partitioning and spatial bootstrapping methods: A case study of innovative startups","authors":"Alessio Bumbea , Andrea Mazzitelli , Giuseppe Espa , Alessandro Rinaldi","doi":"10.1016/j.bdr.2025.100533","DOIUrl":"10.1016/j.bdr.2025.100533","url":null,"abstract":"<div><div>Innovative startups are the source of innovation and technological development; therefore, understanding their behavior can help better recognize the business organization's direction. This paper introduces a new method for clustering innovative startups using bipartite graph partitioning combined with spatial bootstrapping, improving clusters' accuracy and interpretability. Recent advancements in clustering techniques have introduced ensemble or consensus clustering methods, which aim to merge multiple clustering results into a superior outcome. A key challenge in this field is effectively integrating diverse clusters, and one promising solution involves utilizing graph formalism and partitioning strategies. By leveraging advanced graph partitioning techniques, we transform the task of partitioning the ensemble graph into a community detection problem. Our methodological approach improves the traditional method of bipartite graphs used in cluster ensembles by implementing the state of the art biLouvain algorithm. We also focused on techniques that could be used to increase the interpretability of the clusters themselves and how they can be used to obtain insightful information from the data. The proposed methodology was applied to a dataset of technologically advanced new businesses, located in the Lombardy region and recorded as innovative startups in the special section of the Italian Chambers of Commerce's Business Register.</div></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"41 ","pages":"Article 100533"},"PeriodicalIF":3.5,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144090526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

E-word of mouth in sales volume forecasting: Toyota Camry case study 电子口碑在销量预测中的应用：丰田凯美瑞案例研究

IF 3.5 3区计算机科学

Big Data Research Pub Date : 2025-05-15 DOI: 10.1016/j.bdr.2025.100542

Domenica Fioredistella Iezzi , Roberto Monte

引用次数: 0

Exploring the impact of high schools, socioeconomic factors, and degree programs on higher education success in Italy 探讨意大利高中、社会经济因素和学位课程对高等教育成功的影响

IF 3.5 3区计算机科学

Big Data Research Pub Date : 2025-05-15 DOI: 10.1016/j.bdr.2025.100539

Cristian Usala, Isabella Sulis, Mariano Porcu

{"title":"Exploring the impact of high schools, socioeconomic factors, and degree programs on higher education success in Italy","authors":"Cristian Usala, Isabella Sulis, Mariano Porcu","doi":"10.1016/j.bdr.2025.100539","DOIUrl":"10.1016/j.bdr.2025.100539","url":null,"abstract":"<div><div>This study investigates the determinants of tertiary education success in Italy, focusing on students' outcomes between the first and second years. We use population data of students enrolled between 2015 and 2019, integrating information on high school environments and degree program characteristics. This rich dataset has been exploited with a two-step approach: the first step defines indicators for high school quality and degree program difficulty; the second estimates a multinomial logit to assess the determinants of students' probability of being classified as regulars, churners, at risk of dropout, and dropouts. Data regarding the 2019 cohort have been further investigated by exploiting the additional information on students' socioeconomic backgrounds and schools' self-assessed effectiveness evaluations. Results indicate that students' high school backgrounds, socioeconomic conditions, and post-graduation prospects in terms of net wages and occupation rates of graduates in the chosen degree program significantly influence academic success and students' academic persistence. Overall, the results offer a comprehensive view of the determinants of university success, with specific patterns observed across the different student categories.</div></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"41 ","pages":"Article 100539"},"PeriodicalIF":3.5,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144134568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Business digitalization in Italy: A comprehensive analysis using supplementary fuzzy set approach 意大利商业数字化：利用补充模糊集方法的综合分析

IF 3.5 3区计算机科学

Big Data Research Pub Date : 2025-05-15 DOI: 10.1016/j.bdr.2025.100538

Ilaria Benedetti, Federico Crescenzi, Tiziana Laureti, Niccolò Salvini

{"title":"Business digitalization in Italy: A comprehensive analysis using supplementary fuzzy set approach","authors":"Ilaria Benedetti, Federico Crescenzi, Tiziana Laureti, Niccolò Salvini","doi":"10.1016/j.bdr.2025.100538","DOIUrl":"10.1016/j.bdr.2025.100538","url":null,"abstract":"<div><div>In an era where digital technologies such as AI, cloud computing and IoT are reshaping global business dynamics, the digital transformation of enterprises has become a pivotal factor for maintaining competitive advantage. This paper provides an in-depth analysis of the digitalization process among Italian firms, leveraging data from the ISTAT ICT survey. Using a fuzzy set approach, we develop a refined index to measure technological deprivation across multiple dimensions, providing a detailed understanding of how digitalization is adopted at the firm level. The results indicate a moderate level of technological development among firms. The dimension related to online sales emerges as the most underdeveloped, highlighting it as a critical area for improvement for Italian companies and underscoring the need for targeted policy interventions to bridge these digital gaps. Moreover, the analysis reveals significant disparities across sectors, geographic areas, and firm sizes, with smaller enterprises and those in certain regions exhibiting lower levels of digital adoption. Our study underscores the utility of the fuzzy set methodology for analyzing high-dimensional big data and provides actionable insights for enhancing digital adoption among firms in Italy.</div></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"41 ","pages":"Article 100538"},"PeriodicalIF":3.5,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144106841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Big data analytics for smart home energy management system based on IOMT using AHP and WASPAS 基于AHP和WASPAS的IOMT智能家居能源管理系统大数据分析

IF 3.5 3区计算机科学

Big Data Research Pub Date : 2025-05-10 DOI: 10.1016/j.bdr.2025.100534

Jingze Zhou , Salem Alkhalaf , S. Abdel-Khalek , Shah Nazir

{"title":"Big data analytics for smart home energy management system based on IOMT using AHP and WASPAS","authors":"Jingze Zhou , Salem Alkhalaf , S. Abdel-Khalek , Shah Nazir","doi":"10.1016/j.bdr.2025.100534","DOIUrl":"10.1016/j.bdr.2025.100534","url":null,"abstract":"<div><div>The convergence of edge computing and 5G network speed provides an innovative way to address the energy efficiency and low latency requirements in medical data processing, especially from the perspective of the Internet of Medical Things (IoMT). Together, these technologies allow for the quick and effective handling of the enormous volumes of medical data produced by different IoMT devices in the context of smart healthcare systems. The IoMT is bringing cutting-edge technologies, social benefits, and economic advantages to transform modern healthcare systems entirely. Digital healthcare is transforming due to machine learning, which uses sophisticated algorithms to forecast patients’ health status efficiently. These approaches predict the onset of disease, hospital readmissions, and treatment customization by analyzing large medical datasets. Strong data security and good forecast accuracy are still issues. The quality and variety of training data are key factors in making accurate predictions, and strict encryption, safe storage, and regulatory compliance are necessary for data security. By including various significant components from existing research, the current study seeks to determine the most collective features. The goal of the study is to offer a systematic approach for assessing these features identified by using the approaches of the AHP and WASPAS. These approaches are effective for efficient big data analytics in the context of smart home energy management system based on IOMT.</div></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"41 ","pages":"Article 100534"},"PeriodicalIF":3.5,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144170001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Saving food surplus and developing new business models: Exploring the potential of ‘Too Good To Go’ at territorial level using web-scraped data 节约粮食剩余和发展新的商业模式：利用网络数据在地区层面探索“太好而不能去”的潜力

IF 3.5 3区计算机科学

Big Data Research Pub Date : 2025-05-10 DOI: 10.1016/j.bdr.2025.100536

Mengting Yu, Luca Secondi, Tiziana Laureti, Luigi Palumbo

引用次数: 0

A novel study of kernel graph regularized semi-non-negative matrix factorization with orthogonal subspace for clustering 基于正交子空间的核图正则化半非负矩阵分解聚类的新研究

IF 3.5 3区计算机科学

Big Data Research Pub Date : 2025-04-22 DOI: 10.1016/j.bdr.2025.100531

Yasong Chen , Wen Li, Junjian Zhao

{"title":"A novel study of kernel graph regularized semi-non-negative matrix factorization with orthogonal subspace for clustering","authors":"Yasong Chen , Wen Li, Junjian Zhao","doi":"10.1016/j.bdr.2025.100531","DOIUrl":"10.1016/j.bdr.2025.100531","url":null,"abstract":"<div><div>As a nonlinear extension of Non-negative Matrix Factorization (NMF), Kernel Non-negative Matrix Factorization (KNMF) has demonstrated greater effectiveness in revealing latent features from raw data. Building on this, this paper introduces kernel theory and effectively combines the advantages of semi-nonnegative constraints, graph regularization, and orthogonal subspace constraints to propose a novel model-Kernel Graph Regularized Semi-Negative Matrix Factorization with Orthogonal Subspaces and Auxiliary Variables (semi-KGNMFOSV). This model introduces auxiliary variables and reformulates the optimization problem, successfully overcoming the convergence proof challenges typically associated with orthogonal subspace-constrained methods. Furthermore, the model utilizes kernel methods to effectively capture complex nonlinear structures in the data. The semi-nonnegative constraint, along with orthogonal subspace constraints incorporating auxiliary variables, enhances optimization efficiency, while graph regularization preserves the local geometric structure of the data. We develop an efficient optimization algorithm to solve the proposed model and conduct extensive experiments on multiple real-world datasets. Additionally, we investigate the impact of three different initialization strategies on the performance of the proposed algorithm. Experimental results demonstrate that, compared to classical and state-of-the-art methods, the proposed model exhibits superior performance across all three initialization strategies.</div></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"40 ","pages":"Article 100531"},"PeriodicalIF":3.5,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143863357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hourglass pattern matching for deep aware neural network text recommendation model 沙漏模式匹配的深度感知神经网络文本推荐模型

IF 3.5 3区计算机科学

Big Data Research Pub Date : 2025-04-17 DOI: 10.1016/j.bdr.2025.100532

Li Gao, Hongjun Li, Qingkui Chen, Dunlu Peng

{"title":"Hourglass pattern matching for deep aware neural network text recommendation model","authors":"Li Gao, Hongjun Li, Qingkui Chen, Dunlu Peng","doi":"10.1016/j.bdr.2025.100532","DOIUrl":"10.1016/j.bdr.2025.100532","url":null,"abstract":"<div><div>In recent years, with the rapid development of deep learning, big data mining, and natural language processing (NLP) technologies, the application of NLP in the field of recommendation systems has attracted significant attention. However, current text recommendation systems still face challenges in handling word distribution assumptions, preprocessing design, network inference models, and text perception technologies. Traditional RNN neural network layers often encounter issues such as gradient explosion or vanishing gradients, which hinder their ability to effectively handle long-term dependencies and reverse text inference among long texts. Therefore, this paper proposes a new type of depth-aware neural network recommendation model (Hourglass Deep-aware neural network Recommendation Model, HDARM), whose structure presents an hourglass shape. This model consists of three parts: The top of the hourglass uses Word Embedding for input through Fine-tune Bert to process text embeddings as word distribution assumptions, followed by utilizing bidirectional LSTM to integrate Transformer models for learning critical information. The middle of the hourglass retains key features of network outputs through CNN layers, which are combined with pooling layers to extract and enhance critical information from user text. The bottom of the hourglass avoids a decline in generalization performance through deep neural network layers. Finally, the model performs pattern matching between text vectors and word embeddings, recommending texts based on their relevance. In experiments, this model improved metrics like MSE and NDCG@10 by 8.74 % and 10.89 % respectively compared to the optimal baseline model.</div></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"40 ","pages":"Article 100532"},"PeriodicalIF":3.5,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143923599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A decision tree algorithm based on adaptive entropy of feature value importance 基于特征值重要度自适应熵的决策树算法

IF 3.5 3区计算机科学

Big Data Research Pub Date : 2025-04-14 DOI: 10.1016/j.bdr.2025.100530

Shaobo Deng, Weili Yuan, Sujie Guan, Xing Lin, Zemin Liao, Min Li

引用次数: 0

TE-PADN: A poisoning attack defense model based on temporal margin samples TE-PADN：基于时差采样的中毒攻击防御模型

IF 3.5 3区计算机科学

Big Data Research Pub Date : 2025-04-09 DOI: 10.1016/j.bdr.2025.100528

Haitao He , Ke Liu , Lei Zhang , Ke Xu , Jiazheng Li , Jiadong Ren

{"title":"TE-PADN: A poisoning attack defense model based on temporal margin samples","authors":"Haitao He , Ke Liu , Lei Zhang , Ke Xu , Jiazheng Li , Jiadong Ren","doi":"10.1016/j.bdr.2025.100528","DOIUrl":"10.1016/j.bdr.2025.100528","url":null,"abstract":"<div><div>With the development of network security research, intrusion detection systems based on deep learning show great potential in network attack detection. As crucial tools for ensuring network information security, these systems themselves are vulnerable to poisoning attacks from attackers. Currently, most poisoning attack defense methods cannot effectively utilize network traffic characteristics and are only effective for specific models, showing poor defense results for other models. Furthermore, detection of poisoning attacks is often overlooked, leading to a lack of timely and effective defense against such attacks. Therefore, we propose a data poisoning defense mechanism called TE-PADN. Firstly, we introduce a temporal margin sample generation algorithm that integrates an attention mechanism. Based on mapping the original data time series into a latent feature space, this algorithm learns the temporal characteristics of the data and focuses on information from different positions using the attention mechanism to generate temporal margin samples for repairing poisoned models. Secondly, we propose a multi-level poisoning attack detection method for real-time and accurate detection of undetected poisoning attacks. By employing ensemble learning methods, this approach enhances model robustness, repairs model classification boundaries that have shifted due to poisoning attacks and achieves efficient defense against poisoning attacks. Finally, experimental validation of our proposed method demonstrates promising results. Under a 10% attack intensity, the average accuracy of TE-PADN in recovering poisoning models increased by 6.5% on the NSL-KDD dataset, 5.3% on the UNSW-NB15 dataset, and 5.9% on the CICIDS2017 dataset.</div></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"40 ","pages":"Article 100528"},"PeriodicalIF":3.5,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143816452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0